Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation
Faustyna Krawiec, Simon Peyton Jones, Neel Krishnaswami, Tom Ellis, Richard A. Eisenberg, Andrew W. Fitzgibbon
2022年份
27被引次数
10顶会引用
摘要
In this paper, we give a simple and efficient implementation of reverse-mode automatic differentiation, which both extends easily to higher-order functions, and has run time and memory consumption linear in the run time of the original program. In addition to a formal description of the translation, we also describe an implementation of this algorithm, and prove its correctness by means of a logical relations argument.
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引用它的顶会 Paper10
- ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic ProgramsAlexander K. Lew, Mathieu Huot, Sam Staton, Vikash K. MansinghkaPOPL 2023 · 被引用 16 次
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- Efficient Dual-Numbers Reverse AD via Well-Known Program TransformationsTom Smeding, Matthijs VákárPOPL 2023 · 被引用 10 次
- A general construction for abstract interpretation of higher-order automatic differentiationJacob Laurel, Rem Yang, Shubham Ugare, Robert Nagel 等OOPSLA 2022 · 被引用 9 次
- On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable ParametersWonyeol Lee, Sejun Park, Alex AikenICML 2023 · 被引用 6 次
它引用的顶会 Paper4
- A simple differentiable programming languageMartín Abadi, Gordon D. PlotkinPOPL 2020 · 被引用 49 次
- Automatic differentiation in PCFDamiano Mazza, Michele PaganiPOPL 2021 · 被引用 47 次
- Backpropagation in the simply typed lambda-calculus with linear negationAloïs Brunel, Damiano Mazza, Michele PaganiPOPL 2020 · 被引用 24 次
- 𝜆ₛ: computable semantics for differentiable programming with higher-order functions and datatypesBenjamin Sherman, Jesse Michel, Michael CarbinPOPL 2021 · 被引用 11 次
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